The Quick-and-Dirty Summary
I was recently asked to participate in a proposed SXSW panel that will debate the question, “Will Data Scientists Be Replaced by Tools?” This post describes my current thinking on that question as a way of (1) convincing you to go vote for the panel’s inclusion in this year’s SXSW and (2) instigating a larger debate about the future of companies whose business model depends upon Data Science in some way.
The Slow-and-Clean Version
In the last five years, Data Science has emerged as a new discipline, although there are many reasonable people who think that this new discipline is largely a rebranding of existing fields that suffer from a history of poor marketing and weak publicity.
All that said, within the startup world, I see at least three different sorts of Data Science work being done that really do constitute new types of activities for startups to be engaged in:
- Data aggregation and redistribution: A group of companies like DataSift have emerged recently whose business model centers on the acquisition of large quantities of data, which they resell in raw or processed form. These companies are essentially the Exxon’s of the data mining world.
- Data analytics toolkit development: Another group of companies like Prior Knowledge have emerged that develop automated tools for data analysis. Often this work involves building usable and scalable implementations of new methods coming out of academic machine learning groups.
- In-house data teams: Many current startups and once-upon-a-time startups now employ at least one person whose job title is Data Scientist. These people are charged with extracting value from the data accumulated by startups as a means of increasing the market value of these startups.
I find these three categories particularly helpful here, because it seems to me that the question, “Will Data Scientists Be Replaced by Tools?”, is most interesting when framed as a question about whether the third category of workers will be replaced by the products designed by the second category of workers. I see no sign that the first category of companies will go away anytime soon.
When posed this way, the most plausible answer to the question seems to be: “data scientists will have portions of their job automated, but their work will be much less automated than one might hope. Although we might hope to replace knowledge workers with algorithms, this will not happen as soon as some would like to claim.”
In general, I’m skeptical of sweeping automation of any specific branch of knowledge workers because I think the major work done by a data scientist isn’t technological, but sociological: their job is to communicate with the heads of companies and with the broader public about how data can be used to improve businesses. Essentially, data scientists are advocates for better data analysis and for more data-driven decision-making, both of which require constant vigilance to maintain. While the mathematical component of the work done by a data scientist is essential, it is nevertheless irrelevant in the absence of human efforts to sway decision-makers.
To put it another way, many of the problems in our society aren’t failures of science or technology, but failures of human nature. Consider, for example, Atul Gawande’s claim that many people still die each year because doctors don’t wash their hands often enough. Even though Seimelweiss showed the world that hygiene is a life-or-death matter in hospitals more than a century ago, we’re still not doing a good enough job maintaining proper hygiene.
Similarly, we can examine the many sloppy uses of basic statistics that can be found in the biological and the social sciences — for example, those common errors that have been recently described by Ioannidis and Simonsohn. Basic statistical methods are already totally automated, but this automation seems to have done little to make the analysis of data more reliable. While programs like SPSS have automated the computational components of statistics, they have done nothing to diminish the need for a person in the loop who understands what is actually being computed and what it actually means about the substantive questions being asked of data.
While we can — and will — develop better tools for data analysis in the coming years, we will not do nearly as much as we hope to obviate the need for sound judgment, domain expertise and hard work. As David Freedman put it, we’re still going to need shoe leather to get useful insights out of data and that will require human intervention for a very long time to come. The data scientist can no more be automated than the CEO.